Building change detection based on multi-scale filtering and grid partition

TL;DR

This paper introduces a two-stage building change detection method using multi-scale filtering and grid partitioning, enabling direct change type identification with improved efficiency over existing multi-index learning approaches.

Contribution

It proposes a novel two-stage approach combining multi-scale filtering and grid-based classification to directly detect building change types, reducing computation and improving accuracy.

Findings

Detects building change types directly

Outperforms existing multi-index learning methods

Efficient and accurate change detection

Abstract

Building change detection is of great significance in high resolution remote sensing applications. Multi-index learning, one of the state-of-the-art building change detection methods, still has drawbacks like incapability to find change types directly and heavy computation consumption of MBI. In this paper, a two-stage building change detection method is proposed to address these problems. In the first stage, a multi-scale filtering building index (MFBI) is calculated to detect building areas in each temporal with fast speed and moderate accuracy. In the second stage, images and the corresponding building maps are partitioned into grids. In each grid, the ratio of building areas in time T2 and time T1 is calculated. Each grid is classified into one of the three change patterns, i.e., significantly increase, significantly decrease and approximately unchanged. Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.